A Fast Approximate Nearest Neighbor Search Algorithm in the Hamming Space

2012 ◽  
Vol 34 (12) ◽  
pp. 2481-2488 ◽  
Author(s):  
Mani Malek Esmaeili ◽  
R. K. Ward ◽  
M. Fatourechi
Keyword(s):  
Nearest Neighbor ◽  
Search Algorithm ◽  
Nearest Neighbor Search ◽  
Approximate Nearest Neighbor Search ◽  
Approximate Nearest Neighbor ◽  
Neighbor Search ◽  
Hamming Space

Optimized K-Means Hashing for Approximate Nearest Neighbor Search

2014 ◽  
Vol 651-653 ◽  
pp. 2168-2171
Author(s):  
Qin Zhen Guo ◽  
Zhi Zeng ◽  
Shu Wu Zhang ◽  
Yuan Zhang ◽  
Gui Xuan Zhang
Keyword(s):  
High Efficiency ◽  
Nearest Neighbor ◽  
Quantization Error ◽  
Nearest Neighbor Search ◽  
Binary Codes ◽  
Neighborhood Structure ◽  
Approximate Nearest Neighbor Search ◽  
Approximate Nearest Neighbor ◽  
Neighbor Search ◽  
Hamming Space

Hashing which maps data into binary codes in Hamming space has attracted more and more attentions for approximate nearest neighbor search due to its high efficiency and reduced storage cost. K-means hashing (KH) is a novel hashing method which firstly quantizes the data by codewords and then uses the indices of codewords to encode the data. However, in KH, only the codewords are updated to minimize the quantization error and affinity error while the indices of codewords remain the same after they are initialized. In this paper, we propose an optimized k-means hashing (OKH) method to encode data by binary codes. In our method, we simultaneously optimize the codewords and the indices of them to minimize the quantization error and the affinity error. Our OKH method can find both the optimal codewords and the optiaml indices, and the resulting binary codes in Hamming space can better preserve the original neighborhood structure of the data. Besides, OKH can further be generalized to a product space. Extensive experiments have verified the superiority of OKH over KH and other state-of-the-art hashing methods.

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